Develop a model for whether the person is wearing sunglasses or not

[UppuluriKalyani]

Detecting whether a person is wearing sunglasses or not in images is a common computer vision task with various practical applications. For example, in security systems, it can help identify individuals in surveillance footage or control access to secure areas. In retail, it can be used for personalized advertising or customer analytics. However, accurately distinguishing between sunglass-wearing and non-sunglass-wearing individuals can be challenging due to factors like varying lighting conditions, poses, and facial occlusions.

[UppuluriKalyani]

@TAHIR0110 Sir, can you please assign this issue to me.

[varshithar12]

I agree to follow this project's Code of Conduct I'm a GSSOC'24 contributor I want to work on this issue.

[rahulnarayaniitk]

Hi @TAHIR0110, I would love to work on this issue as a part of GSSoC '24 program.

Developing a model to detect whether a person is wearing sunglasses or not is an interesting computer vision task. Here's a high-level outline of how I will be approaching it:

1. Data Collection: Gather a dataset of images containing people, some wearing sunglasses and some without. Ensure that the dataset is diverse in terms of lighting conditions, backgrounds, angles, and types of sunglasses.

2. Data Preprocessing: Preprocess the images to standardize their size, color, and orientation. We may also need to label the images to indicate whether sunglasses are present or not.

3. Feature Extraction: Using techniques such as convolutional neural networks (CNNs) to automatically learn relevant features from the images. CNNs are well-suited for image classification tasks and can effectively capture hierarchical patterns.

4. Model Training: Split your dataset into training, validation, and testing sets. We will then train our model on the training data and use the validation set to tune hyperparameters and prevent overfitting. Further we will evaluate the model's performance on the testing set.

5. Model Evaluation: Assess the performance of our model using metrics such as accuracy, precision, recall, and F1-score. Additionally, visualize the model's predictions to gain insights into its strengths and weaknesses.

6. Fine-Tuning and Optimization: Experiment with different architectures, optimization algorithms, and data augmentation techniques to improve the model's performance further.

By following these steps, I will be developing a robust model for detecting whether a person is wearing sunglasses or not, which can have various applications in areas such as security, fashion, and image processing.

@TAHIR0110, I request you to kindly assign this issue to me so that I can start working on it as soon as possible.